Alation

MARCH 14, 2023

All critical data elements (CDEs) should be collated and inventoried with relevant metadata, then classified into relevant categories and curated as we further define below. Govern Data governance models should be flexible and dynamic while proactively addressing risk management and compliance with local and global regulations.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

CIO Business Intelligence

APRIL 29, 2022

From a policy perspective, the organization needs to mature beyond a basic awareness and definition of data compliance requirements (which typically holds that local operations make data “sovereign” by default) to a more refined, data-first model that incorporates corporate risk management, regulatory and reporting issues, and compliance frameworks.

Digital Transformation 132

Digital Transformation Strategy Data-driven Data Strategy 132

CIO Business Intelligence

SEPTEMBER 2, 2022

Back-end software engineers are responsible for maintaining the structure of server-side information by optimizing servers, implementing security measures, and developing data storage solutions. Back-end software engineer.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

Back-end software engineers are responsible for maintaining the structure of server-side information by optimizing servers, implementing security measures, and developing data storage solutions. Back-end software engineer.

Finance 98

Finance IT Software Reporting 98

Sisense

SEPTEMBER 3, 2020

Data Scientist. Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Data analysts in one organization might be called data scientists or statisticians in another.

Statistics 97

Statistics Metrics Visualization Finance 97

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Note that the emphasis of SR 11-7 is on risk management.). Image by Ben Lorica.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

Data Architecture 83

Data Architecture Risk Management Risk Management 83

Cloudera

SEPTEMBER 7, 2023

With the emergence of new creative AI algorithms like large language models (LLM) fromOpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT—awareness, interest and adoption of AI use cases across industries is at an all time high. But these measures alone may not be sufficient to protect proprietary information.

Insurance 81

Insurance Risk Data-driven Finance 81

DataRobot Blog

JULY 21, 2022

Outline clear metrics to measure success. Document assumptions and risks to develop a risk management strategy. Machine learning models created in silos are rarely implemented. Evaluate the computing resources and development environment that the data science team will need. Exploring and Transforming Data.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

erwin

FEBRUARY 4, 2021

However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights. Overcoming Data Governance Bottlenecks. Put data quality first : Users must have confidence in the data they use for analytics.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

Domino Data Lab

FEBRUARY 4, 2019

Lately there’s been lots of dialog about bias in machine learning models, though considering the noise part of the equation is an important corollary. We’d love to hear your insights and perspectives about running data teams. The ability to measure results (risk-reducing evidence). Legal concerns, risk, compliance.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

datapine

AUGUST 29, 2022

Data Smart’ contains enough practical knowledge to actually start performing analyses by using good old Microsoft Excel. The author then proceeds to highlight the most important steps of the process model, such as sampling, treatment of missing values, and variable selection.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

AWS Big Data

AUGUST 14, 2023

Taken together, the posts in this series lay out some possible operating models for data mesh within an organization. In the same way, organizations seeking to implement successful data mesh strategies must respect the nature and structure (legal, political, commercial, technology) of their organizations in their implementation.

Management 79

Management Risk Measurement Optimization 79

Andrew White

JANUARY 11, 2021

How do you think Technology Business Management plays into this strategy? Where does the Data Architect role fits in the Operational Model ? Assuming a data architect helps model and guide and assist D&A then they play a key role. Value Management or monetization. Product Management. Governance.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52